Use of empirical global radiation models for maize growth simulation

Agricultural and Forest Meteorology

Volumn 126, Issue 1-2, 2004, Pages 47-58

  Pohlert, Thorsten ^a  

^a JUSTUS LIEBIG UNIVERSITY   (Germany)

Author keywords

Crop growth model; Global radiation models; Missing value problem; Sensitivity study; WOFOST

Indexed keywords

AGROECOLOGY; CROP YIELD; GROWTH MODELING; MAIZE; PHOTOSYNTHESIS; RADIATION BUDGET;

ZEA MAYS;

EID: 7044272741     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2004.05.003     Document Type: Article

References (25)

1. Allen, R., 1995. Evaluation of procedures of estimating mean monthly solar radiation from air temperature. FAO, Rome.
2. Allen R. Self-calibrating method for estimating solar radiation from air temperature. J. Hydrol. Eng. 2:1997;57-67
3. Ångstrøm A. Solar and terrestrial radiation. Q.J.R. Meterol. Soc. 50:1924;121-125
4. Boogard, H.L., Van Diepen, C.A., Rötter, R.P., Cabrera, J.M.C.A., Van Laar, H.H., 1998. User's guide for the WOFOST 7.1. crop growth simulation model and WOFOST Control Center 1.5. SC-DLO Technical Document 52, Wageningen, p. 127
5. Boote K.J., Jones J.W., Pickering N.B. Potential uses and limitations of crop models. Agron. J. 88:1996;704-716
6. Bristow K., Campbell G. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agric. Forest Meterol. 31:1984;159-166
7. Hoogenboom G. Contribution of agrometeorology to the simulation of crop production and its applications. Agric. Forest Meterol. 103:2000;137-157
8. Donatelli M., Bellocchi G., Fontana F. RadEst3.00: software to estimate daily radiation data from commonly available meteorological variables. Eur. J. Agron. 18:2003;363-367
9. Doorenbos, J., Pruitt, W.O., 1977. Guidelines for predicting crop water requirements. FAO Irrigation and drainage paper 24, Rome
10. Dubrovský M., Žalud Z., Štastná M. Sensitivity of CERES-maize yields to statistical structure of daily weather series. Clim. Change. 46:2000;447-472
11. Grossi Gallegos, H., Roberti, A., Renzini, G., 2001. Comparacion estadística de las bases de datos de irradiacion solar disponibles en la Republica Argentina. In: AADA (Eds. 2001), Anales. VIII Reunion Argentina de Agrometeorología. La Agrometeorología hacia el Siglo XXI, Mendoza (CD-Rom Edition).
12. Jensen, M.E., Burman, R.D., Allen, R. (Eds.), 1990. Evapotranspiration and irrigation water requirements, ASCE Manuals and reports on engineering practice 70, New York, p. 332
13. Jones, C.A., Kiniry, J.R., 1986. CERES-Maize: A Simulation Model of Maize Growth and Development. Texas A&M University Press, College Station, p. 194
14. Magrin G.O., Hall A.J., Baldy C., Grondona M.O. Spatial and interannual variations in the photothermal quotient: implications for the potential kernel number of wheat crops in Argentina. Agric. Forest Meteorol. 67:1993;29-41
15. Meek D.W., Hatfield J.L. Data quality checking for single station meteorological databases. Agric. Forest Meterol. 69:1994;85-109
16. Meza F., Varas E. Estimation of mean monthly solar global radiation as a function of temperature. Agric. Forest Meterol. 100:2000;231-241
17. Nonhebel, S., 1993. The importance of weather data in crop growth simulation models and assessment of climatic change effects. Ph.D. thesis, University of Wageningen, p. 144
18. Nonhebel S. The effects of use of average instead of daily weather data in Crop Growth Simulation Models. Agric. Sys. 44:1994;377-396
19. Pohlert, T., 2002. Simulation von Ertragsrisiken für Weizen und Mais in Argentinien. Diplomarbeit University, Trier, p. 125
20. Rötter, R., 1993. Simulation of biophysical limitations to maize production under rainfed conditions in Kenya, evaluation and application of the model WOFOST. Materialien zur Ostafrikaforschung 12, Trier, p. 261
21. Sachs, L., 1991. Angewandte Statistik, eigth ed., Springer, Berlin, p. 883
22. Shisanya, C.A., 1996. Chances and risks of maize and beans growing in the semi-arid areas of south-east Kenya during deficient, normal and above-normal rainfall of the short rainy seasons. Materialien zur Ostafrikafoschung 14, p. 417
23. Supit, I., Hooijer, A.A., Van Diepen, C.A. (Eds.) 1994. System description of the WOFOST 6.0 crop simulation model implemented in CGMS. Joint Research Centre EC, Brussels, p. 144
24. Tsuji, G.Y., Uehara, G., Balas, S., 1994. DSSAT v3. University of Hawaii, Honolulu, Hawaii.
25. Van Ittersum M.K., Leffelaar P.A., Van Keulen H., Kropff M.J., Bastiaans L., Goudriaan J. On approaches and applications of the Wageningen crop models. Eur. J. Agron. 18:2003;201-234